def test_bad_inputs(self):
template = (
"MACDSignal() expected a value greater than or equal to 1"
" for argument %r, but got 0 instead."
)
with self.assertRaises(ValueError) as e:
MovingAverageConvergenceDivergenceSignal(fast_period=0)
self.assertEqual(template % 'fast_period', str(e.exception))
with self.assertRaises(ValueError) as e:
MovingAverageConvergenceDivergenceSignal(slow_period=0)
self.assertEqual(template % 'slow_period', str(e.exception))
with self.assertRaises(ValueError) as e:
MovingAverageConvergenceDivergenceSignal(signal_period=0)
self.assertEqual(template % 'signal_period', str(e.exception))
with self.assertRaises(ValueError) as e:
MovingAverageConvergenceDivergenceSignal(
fast_period=5,
slow_period=4,
)
expected = (
"'slow_period' must be greater than 'fast_period', but got\n"
"slow_period=4, fast_period=5"
)
self.assertEqual(expected, str(e.exception))
def test_moving_average_convergence_divergence(self,
seed,
fast_period,
slow_period,
signal_period):
rng = RandomState(seed)
nassets = 3
macd = MovingAverageConvergenceDivergenceSignal(
fast_period=fast_period,
slow_period=slow_period,
signal_period=signal_period,
)
today = pd.Timestamp('2016', tz='utc')
assets = pd.Index(np.arange(nassets))
out = np.empty(shape=(nassets,), dtype=np.float64)
close = rng.rand(macd.window_length, nassets)
macd.compute(
today,
assets,
out,
close,
fast_period,
slow_period,
signal_period,
)
close_df = pd.DataFrame(close)
fast_ewma = self.expected_ewma(
close_df,
fast_period,
)
slow_ewma = self.expected_ewma(
close_df,
slow_period,
)
signal_ewma = self.expected_ewma(
fast_ewma - slow_ewma,
signal_period
)
# Everything but the last row should be NaN.
self.assertTrue(signal_ewma.iloc[:-1].isnull().all().all())
# We're testing a single compute call, which we expect to be equivalent
# to the last row of the frame we calculated with pandas.
expected_signal = signal_ewma.values[-1]
np.testing.assert_almost_equal(
out,
expected_signal,
decimal=8
)
def make_pipeline():
all_assets_filter = (USEquityPricing.close.latest > 0)
returns_5 = Returns(window_length=5)
rsi = RSI(inputs=[USEquityPricing.close])
macd = MovingAverageConvergenceDivergenceSignal(mask=all_assets_filter)
ema = ExponentialWeightedMovingAverage(mask=all_assets_filter,
inputs=[USEquityPricing.close],
window_length=30,
decay_rate=(1 - (2.0 / (1 + 15.0))))
mean_5 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=5,
mask=all_assets_filter)
mean_10 = SimpleMovingAverage(inputs=[USEquityPricing.close],
window_length=10,
mask=all_assets_filter)
bb = BollingerBands(inputs=[USEquityPricing.close], window_length=20, k=2)
return Pipeline(
columns={
'returns_5': returns_5,
'RSI': rsi,
'MACD': macd,
'EMA': ema,
'SMA_5': mean_5,
'SMA_10': mean_10,
'bb_upper': bb.upper,
'bb_middle': bb.middle,
'bb_lower': bb.lower
},
screen=all_assets_filter,
)
def test_bad_inputs(self):
template = ("MACDSignal() expected a value greater than or equal to 1"
" for argument %r, but got 0 instead.")
with pytest.raises(ValueError,
match=re.escape(template % "fast_period")):
MovingAverageConvergenceDivergenceSignal(fast_period=0)
with pytest.raises(ValueError,
match=re.escape(template % "slow_period")):
MovingAverageConvergenceDivergenceSignal(slow_period=0)
with pytest.raises(ValueError,
match=re.escape(template % "signal_period")):
MovingAverageConvergenceDivergenceSignal(signal_period=0)
err_msg = (
"'slow_period' must be greater than 'fast_period', but got\n"
"slow_period=4, fast_period=5")
with pytest.raises(ValueError, match=err_msg):
MovingAverageConvergenceDivergenceSignal(
fast_period=5,
slow_period=4,
)
def test_MACD_window_length_generation(self, seed):
rng = RandomState(seed)
signal_period = rng.randint(1, 90)
fast_period = rng.randint(signal_period + 1, signal_period + 100)
slow_period = rng.randint(fast_period + 1, fast_period + 100)
ewma = MovingAverageConvergenceDivergenceSignal(
fast_period=fast_period,
slow_period=slow_period,
signal_period=signal_period,
)
assert_equal(
ewma.window_length,
slow_period + signal_period - 1,
)